{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = pd.read_csv(r'C:\\Users\\Dell\\Documents\\GitHub\\pinns_opf\\data\\interim\\lines_33.csv')\n",
    "nodes = pd.read_csv(r'C:\\Users\\Dell\\Documents\\GitHub\\pinns_opf\\data\\interim\\nodes_33.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cons_id = 0\n",
    "cons_id_node = {}\n",
    "\n",
    "pv_id = 0\n",
    "pv_id_node = {}\n",
    "\n",
    "ev_id = 0\n",
    "ev_id_node = {}\n",
    "\n",
    "dg_id = 0\n",
    "dg_id_node = {}\n",
    "\n",
    "ess_id = 0\n",
    "ess_id_node = {}\n",
    "for row in nodes.iterrows():\n",
    "\n",
    "    for j in range(len(row[1])):\n",
    "\n",
    "        if row[1][j] == 'cons':\n",
    "            cons_id += 1\n",
    "            cons_id_node[cons_id] = row[1][0]\n",
    "        \n",
    "        if row[1][j] == 'pv':\n",
    "            pv_id += 1\n",
    "            pv_id_node[pv_id] = row[1][0]\n",
    "        \n",
    "        if row[1][j] == 'ev':\n",
    "            ev_id += 1\n",
    "            ev_id_node[ev_id] = row[1][0]\n",
    "\n",
    "        if row[1][j] == 'ess':\n",
    "            ess_id += 1\n",
    "            ess_id_node[ess_id] = row[1][0]\n",
    "        \n",
    "        if row[1][j] == 'dg':\n",
    "            dg_id += 1\n",
    "            dg_id_node[dg_id] = row[1][0]\n",
    "\n",
    "\n",
    "dg_id_node = np.array(list(dg_id_node.items())).T\n",
    "ess_id_node = np.array(list(ess_id_node.items())).T\n",
    "pv_id_node = np.array(list(pv_id_node.items())).T\n",
    "ev_id_node = np.array(list(ev_id_node.items())).T\n",
    "cons_id_node = np.array(list(cons_id_node.items())).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n",
       "        33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n",
       "        49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
       "        65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,\n",
       "        81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95],\n",
       "       [ 2,  2,  3,  3,  3,  4,  4,  4,  4,  5,  5,  5,  6,  6,  7,  7,\n",
       "         8,  8,  8,  9,  9,  9,  9, 10, 10, 10, 11, 11, 11, 12, 12, 13,\n",
       "        13, 14, 14, 14, 15, 15, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19,\n",
       "        19, 20, 20, 20, 20, 21, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23,\n",
       "        23, 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27,\n",
       "        28, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 33, 33]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cons_id_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "node_cons_id = np.array((cons_id_node[1],cons_id_node[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  2,  3,  3,  3,  4,  4,  4,  4,  5,  5,  5,  6,  6,  7,  7,  8,\n",
       "        8,  8,  9,  9,  9,  9, 10, 10, 10, 11, 11, 11, 12, 12, 13, 13, 14,\n",
       "       14, 14, 15, 15, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, 20, 20,\n",
       "       20, 20, 21, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24,\n",
       "       25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 28, 28, 28, 29, 29,\n",
       "       30, 30, 31, 31, 32, 32, 33, 33, 33, 33])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cons_id_node[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('variable_data.json') as f:\n",
    "    dataset = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sequence_ders(dataset, key1, key2, key3):\n",
    "    # Assuming you have two NumPy arrays\n",
    "    data_key1 = np.array(list(dataset[key1].values())).T\n",
    "    data_key2 = np.array(list(dataset[key2].values())).T\n",
    "\n",
    "    # Create a sequence of dictionaries\n",
    "    sequence = []\n",
    "\n",
    "    for i in range(data_key1.shape[0]):\n",
    "        # Stack and transpose the elements\n",
    "        stacked_elements = np.vstack((data_key1[i], data_key2[i])).T\n",
    "\n",
    "        # Create a dictionary with 'dg' as the key and stacked_elements as the value\n",
    "        dictionary = {key3 : stacked_elements}\n",
    "\n",
    "        # Append the dictionary to the sequence\n",
    "        sequence.append(dictionary)\n",
    "    \n",
    "    return sequence\n",
    "\n",
    "def create_sequence_nodes(dataset, key1, key2):\n",
    "    # Assuming you have two NumPy arrays\n",
    "    data_key1 = np.array(list(dataset[key1].values())).T\n",
    "\n",
    "    # Create a sequence of dictionaries\n",
    "    sequence = []\n",
    "\n",
    "    for i in range(data_key1.shape[0]):\n",
    "        # Stack and transpose the elements\n",
    "        elements = np.array(data_key1[i]).T\n",
    "\n",
    "        # Create a dictionary with 'dg' as the key and stacked_elements as the value\n",
    "        dictionary = {key2 : elements}\n",
    "        # Append the dictionary to the sequence\n",
    "        sequence.append(dictionary)\n",
    "    \n",
    "    return sequence\n",
    "\n",
    "# Example usage\n",
    "sequence_dg = create_sequence_ders(dataset, 'dg_x', 'Qdg','dg')\n",
    "sequence_cons = create_sequence_ders(dataset, 'cons_x', 'Qcons','cons')\n",
    "sequence_ess = create_sequence_ders(dataset, 'ess_x', 'Qess', 'ess')\n",
    "sequence_ev = create_sequence_ders(dataset, 'Pev', 'Qev', 'ev')\n",
    "sequence_pv = create_sequence_ders(dataset, 'pv_x', 'Qpv', 'pv')\n",
    "sequence_nodes = create_sequence_nodes(dataset, 'V', 'nodes')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_sequence = []  \n",
    "\n",
    "for dict1, dict2 in zip(sequence_cons, sequence_nodes):\n",
    "    combined_dict = {**dict1, **dict2}  # Merge the dictionaries\n",
    "    combined_sequence.append(combined_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric.data import HeteroData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sequence_cons_x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m data \u001b[39m=\u001b[39m HeteroData()\n\u001b[1;32m----> 2\u001b[0m data[\u001b[39m'\u001b[39m\u001b[39mcons\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mx \u001b[39m=\u001b[39m sequence_cons_x\n\u001b[0;32m      3\u001b[0m data[\u001b[39m'\u001b[39m\u001b[39mcons\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39my \u001b[39m=\u001b[39m sequence_cons_y\n\u001b[0;32m      4\u001b[0m data[\u001b[39m'\u001b[39m\u001b[39mnodes\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mx \u001b[39m=\u001b[39m sequence_nodes[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'sequence_cons_x' is not defined"
     ]
    }
   ],
   "source": [
    "data = HeteroData()\n",
    "data['cons'].x = sequence_cons_x\n",
    "data['cons'].y = sequence_cons_y\n",
    "data['nodes'].x = sequence_nodes[:-1]\n",
    "data['nodes'].y = sequence_nodes[1:]\n",
    "\n",
    "data['cons','connects','nodes'].edge_index = cons_id_node\n",
    "metadata = data.metadata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['cons', 'nodes'], [('cons', 'connects', 'nodes')])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric_temporal.signal import StaticHeteroGraphTemporalSignal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_index_dict = {\n",
    "    (\"cons\",\"connect\",\"nodes\") : cons_id_node\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_dicts = combined_sequence[:-1]\n",
    "target_dicts = combined_sequence[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_snapshots = StaticHeteroGraphTemporalSignal(edge_index_dict, None, feature_dicts, target_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric_temporal.signal import temporal_signal_split\n",
    "\n",
    "train_dataset, test_dataset = temporal_signal_split(graph_snapshots, train_ratio=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'edge_index_dict': {('cons',\n",
       "   'connect',\n",
       "   'nodes'): array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n",
       "          33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n",
       "          49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
       "          65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,\n",
       "          81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95],\n",
       "         [ 2,  2,  3,  3,  3,  4,  4,  4,  4,  5,  5,  5,  6,  6,  7,  7,\n",
       "           8,  8,  8,  9,  9,  9,  9, 10, 10, 10, 11, 11, 11, 12, 12, 13,\n",
       "          13, 14, 14, 14, 15, 15, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19,\n",
       "          19, 20, 20, 20, 20, 21, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23,\n",
       "          23, 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27,\n",
       "          28, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 33, 33]])},\n",
       " 'edge_weight_dict': None,\n",
       " 'feature_dicts': [{'cons': array([[9.99878236e-02, 9.11519008e-17],\n",
       "          [9.99878236e-02, 3.40494194e-10],\n",
       "          [9.99878236e-02, 3.40494271e-10],\n",
       "          [9.99878236e-02, 3.40494271e-10],\n",
       "          [9.99878236e-02, 3.40494271e-10],\n",
       "          [9.99878236e-02, 3.40494507e-10],\n",
       "          [9.99878236e-02, 3.40494507e-10],\n",
       "          [9.99878236e-02, 3.40494507e-10],\n",
       "          [9.99878236e-02, 3.40494507e-10],\n",
       "          [9.99878236e-02, 3.40494700e-10],\n",
       "          [9.99878236e-02, 3.40494700e-10],\n",
       "          [9.99878236e-02, 3.40494700e-10],\n",
       "          [9.99878236e-02, 3.40494969e-10],\n",
       "          [9.99878236e-02, 3.40494969e-10],\n",
       "          [9.99878236e-02, 3.40494960e-10],\n",
       "          [9.99878236e-02, 3.40494960e-10],\n",
       "          [9.99878236e-02, 3.40494928e-10],\n",
       "          [9.99878236e-02, 3.40494928e-10],\n",
       "          [9.99878236e-02, 3.40494928e-10],\n",
       "          [9.99878236e-02, 3.40495000e-10],\n",
       "          [9.99878236e-02, 3.40495000e-10],\n",
       "          [9.99878236e-02, 3.40495000e-10],\n",
       "          [9.99878236e-02, 3.40495000e-10],\n",
       "          [9.99878236e-02, 3.40495026e-10],\n",
       "          [9.99878236e-02, 3.40495026e-10],\n",
       "          [9.99878236e-02, 3.40495026e-10],\n",
       "          [9.99878236e-02, 3.40495110e-10],\n",
       "          [9.99878236e-02, 3.40495110e-10],\n",
       "          [9.99878236e-02, 3.40495110e-10],\n",
       "          [9.99878236e-02, 3.40495296e-10],\n",
       "          [9.99878236e-02, 3.40495296e-10],\n",
       "          [9.99878236e-02, 3.40495475e-10],\n",
       "          [9.99878236e-02, 3.40495475e-10],\n",
       "          [9.99878236e-02, 3.40495620e-10],\n",
       "          [9.99878236e-02, 3.40495620e-10],\n",
       "          [9.99878236e-02, 3.40495620e-10],\n",
       "          [9.99878236e-02, 3.40495732e-10],\n",
       "          [9.99878236e-02, 3.40495732e-10],\n",
       "          [9.99878236e-02, 3.40495732e-10],\n",
       "          [9.99878236e-02, 3.40495732e-10],\n",
       "          [9.99878236e-02, 3.40495799e-10],\n",
       "          [9.99878236e-02, 3.40495799e-10],\n",
       "          [9.99878236e-02, 3.40495847e-10],\n",
       "          [9.99878236e-02, 3.40495847e-10],\n",
       "          [9.99878236e-02, 3.40495871e-10],\n",
       "          [9.99878236e-02, 3.40495871e-10],\n",
       "          [9.99878236e-02, 3.40494356e-10],\n",
       "          [9.99878236e-02, 3.40494356e-10],\n",
       "          [9.99878236e-02, 3.40494356e-10],\n",
       "          [9.99878236e-02, 3.40494493e-10],\n",
       "          [9.99878236e-02, 3.40494493e-10],\n",
       "          [9.99878236e-02, 3.40494493e-10],\n",
       "          [9.99878236e-02, 3.40494493e-10],\n",
       "          [9.99878236e-02, 3.40494580e-10],\n",
       "          [9.99878236e-02, 3.40494580e-10],\n",
       "          [9.99878236e-02, 3.40494580e-10],\n",
       "          [9.99878236e-02, 3.40494580e-10],\n",
       "          [9.99878236e-02, 3.40494625e-10],\n",
       "          [9.99878236e-02, 3.40494625e-10],\n",
       "          [9.99878236e-02, 3.40494625e-10],\n",
       "          [9.99878236e-02, 3.40494625e-10],\n",
       "          [9.99878236e-02, 3.40494260e-10],\n",
       "          [9.99878236e-02, 3.40494260e-10],\n",
       "          [9.99878236e-02, 3.40494260e-10],\n",
       "          [9.99878236e-02, 3.40494260e-10],\n",
       "          [9.99878236e-02, 3.40494204e-10],\n",
       "          [9.99878236e-02, 3.40494204e-10],\n",
       "          [9.99878236e-02, 3.40494204e-10],\n",
       "          [9.99878236e-02, 3.40494249e-10],\n",
       "          [9.99878236e-02, 3.40494249e-10],\n",
       "          [9.99878236e-02, 3.40494249e-10],\n",
       "          [9.99878236e-02, 3.40494249e-10],\n",
       "          [9.99878236e-02, 3.40495219e-10],\n",
       "          [9.99878236e-02, 3.40495219e-10],\n",
       "          [9.99878236e-02, 3.40495219e-10],\n",
       "          [9.99878236e-02, 3.40495219e-10],\n",
       "          [9.99878236e-02, 3.40495426e-10],\n",
       "          [9.99878236e-02, 3.40495426e-10],\n",
       "          [9.99878236e-02, 3.40495426e-10],\n",
       "          [9.99878236e-02, 3.40495426e-10],\n",
       "          [9.99878236e-02, 3.40495597e-10],\n",
       "          [9.99878236e-02, 3.40495597e-10],\n",
       "          [9.99878236e-02, 3.40495597e-10],\n",
       "          [9.99878236e-02, 3.40495733e-10],\n",
       "          [9.99878236e-02, 3.40495733e-10],\n",
       "          [9.99878236e-02, 3.40495844e-10],\n",
       "          [9.99878236e-02, 3.40495844e-10],\n",
       "          [9.99878236e-02, 3.40495936e-10],\n",
       "          [9.99878236e-02, 3.40495936e-10],\n",
       "          [9.99878236e-02, 3.40496003e-10],\n",
       "          [9.99878236e-02, 3.40496003e-10],\n",
       "          [9.99878236e-02, 3.40496048e-10],\n",
       "          [9.99878236e-02, 3.40496048e-10],\n",
       "          [9.99878236e-02, 3.40496048e-10],\n",
       "          [9.99878236e-02, 3.40496048e-10]]),\n",
       "   'nodes': array([1.00111301, 1.00111301, 1.00111301, 1.00111301, 1.00111301,\n",
       "          1.00111302, 1.00111302, 1.00111302, 1.00111302, 1.00111302,\n",
       "          1.00111302, 1.00111302, 1.00111302, 1.00111302, 1.00111302,\n",
       "          1.00111302, 1.00111302, 1.00111302, 1.00111301, 1.00111301,\n",
       "          1.00111301, 1.00111301, 1.00111301, 1.00111301, 1.00111301,\n",
       "          1.00111302, 1.00111302, 1.00111302, 1.00111302, 1.00111302,\n",
       "          1.00111302, 1.00111302, 1.00111302])},\n",
       "  {'cons': array([[9.99851473e-02, 1.12875311e-16],\n",
       "          [9.99851473e-02, 3.62077718e-10],\n",
       "          [9.99851473e-02, 3.62077840e-10],\n",
       "          [9.99851473e-02, 3.62077840e-10],\n",
       "          [9.99851473e-02, 3.62077840e-10],\n",
       "          [9.99851473e-02, 3.62078102e-10],\n",
       "          [9.99851473e-02, 3.62078102e-10],\n",
       "          [9.99851473e-02, 3.62078102e-10],\n",
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       "          [9.99942229e-02, 2.94741151e-04],\n",
       "          [9.99942229e-02, 3.61387754e-04],\n",
       "          [9.99942229e-02, 3.61387754e-04],\n",
       "          [9.99942229e-02, 4.99190747e-04],\n",
       "          [9.99942229e-02, 4.99190747e-04],\n",
       "          [9.99942229e-02, 2.19813831e-03],\n",
       "          [9.99942229e-02, 2.19813831e-03],\n",
       "          [9.99942229e-02, 1.81734593e-04],\n",
       "          [9.99942229e-02, 1.81734593e-04],\n",
       "          [9.99942229e-02, 1.81734593e-04],\n",
       "          [9.99942229e-02, 2.01599652e-04],\n",
       "          [9.99942229e-02, 2.01599652e-04],\n",
       "          [9.99942229e-02, 2.01599652e-04],\n",
       "          [9.99942229e-02, 2.01599652e-04],\n",
       "          [9.99942229e-02, 2.72541843e-04],\n",
       "          [9.99942229e-02, 2.72541843e-04],\n",
       "          [9.99942229e-02, 2.72541843e-04],\n",
       "          [9.99942229e-02, 2.72541843e-04],\n",
       "          [9.99942229e-02, 7.26804164e-04],\n",
       "          [9.99942229e-02, 7.26804164e-04],\n",
       "          [9.99942229e-02, 7.26804164e-04],\n",
       "          [9.99942229e-02, 7.26804164e-04],\n",
       "          [9.99942229e-02, 1.58136757e-04],\n",
       "          [9.99942229e-02, 1.58136757e-04],\n",
       "          [9.99942229e-02, 1.58136757e-04],\n",
       "          [9.99942229e-02, 1.58136757e-04],\n",
       "          [9.99942229e-02, 1.58232490e-04],\n",
       "          [9.99942229e-02, 1.58232490e-04],\n",
       "          [9.99942229e-02, 1.58232490e-04],\n",
       "          [9.99942229e-02, 3.67770868e-04],\n",
       "          [9.99942229e-02, 3.67770868e-04],\n",
       "          [9.99942229e-02, 3.67770868e-04],\n",
       "          [9.99942229e-02, 3.67770868e-04],\n",
       "          [9.99942229e-02, 1.75987139e-04],\n",
       "          [9.99942229e-02, 1.75987139e-04],\n",
       "          [9.99942229e-02, 1.75987139e-04],\n",
       "          [9.99942229e-02, 1.75987139e-04],\n",
       "          [9.99942229e-02, 1.88476011e-04],\n",
       "          [9.99942229e-02, 1.88476011e-04],\n",
       "          [9.99942229e-02, 1.88476011e-04],\n",
       "          [9.99942229e-02, 1.88476011e-04],\n",
       "          [9.99942229e-02, 2.00400407e-04],\n",
       "          [9.99942229e-02, 2.00400407e-04],\n",
       "          [9.99942229e-02, 2.00400407e-04],\n",
       "          [9.99942229e-02, 2.17322599e-04],\n",
       "          [9.99942229e-02, 2.17322599e-04],\n",
       "          [9.99942229e-02, 2.46030099e-04],\n",
       "          [9.99942229e-02, 2.46030099e-04],\n",
       "          [9.99942229e-02, 2.84217970e-04],\n",
       "          [9.99942229e-02, 2.84217970e-04],\n",
       "          [9.99942229e-02, 4.06446474e-04],\n",
       "          [9.99942229e-02, 4.06446474e-04],\n",
       "          [9.99942229e-02, 1.16149775e-03],\n",
       "          [9.99942229e-02, 1.16149775e-03],\n",
       "          [9.99942229e-02, 1.16149775e-03],\n",
       "          [9.99942229e-02, 1.16149775e-03]]),\n",
       "   'nodes': array([1.00036978, 1.00036978, 1.00021416, 1.00028281, 1.00035223,\n",
       "          1.00059939, 1.00072016, 1.0007647 , 1.00089837, 1.001023  ,\n",
       "          1.00103333, 1.00105262, 1.00121578, 1.00131092, 1.00137435,\n",
       "          1.00142945, 1.0015829 , 1.00162461, 1.00038361, 1.00049123,\n",
       "          1.00052284, 1.00056788, 0.99998865, 0.99953648, 0.99955353,\n",
       "          1.00061479, 1.00063466, 1.00075125, 1.00083177, 1.00085962,\n",
       "          1.00095555, 1.00098821, 1.00102892])},\n",
       "  {'cons': array([[9.99911514e-02, 7.36619439e-15],\n",
       "          [9.99911514e-02, 3.85785280e-10],\n",
       "          [9.99911514e-02, 3.85785434e-10],\n",
       "          [9.99911514e-02, 3.85785434e-10],\n",
       "          [9.99911514e-02, 3.85785434e-10],\n",
       "          [9.99911514e-02, 3.85785713e-10],\n",
       "          [9.99911514e-02, 3.85785713e-10],\n",
       "          [9.99911514e-02, 3.85785713e-10],\n",
       "          [9.99911514e-02, 3.85785713e-10],\n",
       "          [9.99911514e-02, 3.85785941e-10],\n",
       "          [9.99911514e-02, 3.85785941e-10],\n",
       "          [9.99911514e-02, 3.85785941e-10],\n",
       "          [9.99911514e-02, 3.85786278e-10],\n",
       "          [9.99911514e-02, 3.85786278e-10],\n",
       "          [9.99911514e-02, 3.85786301e-10],\n",
       "          [9.99911514e-02, 3.85786301e-10],\n",
       "          [9.99911514e-02, 3.85786300e-10],\n",
       "          [9.99911514e-02, 3.85786300e-10],\n",
       "          [9.99911514e-02, 3.85786300e-10],\n",
       "          [9.99911514e-02, 3.85786395e-10],\n",
       "          [9.99911514e-02, 3.85786395e-10],\n",
       "          [9.99911514e-02, 3.85786395e-10],\n",
       "          [9.99911514e-02, 3.85786395e-10],\n",
       "          [9.99911514e-02, 3.85786440e-10],\n",
       "          [9.99911514e-02, 3.85786440e-10],\n",
       "          [9.99911514e-02, 3.85786440e-10],\n",
       "          [9.99911514e-02, 3.85786554e-10],\n",
       "          [9.99911514e-02, 3.85786554e-10],\n",
       "          [9.99911514e-02, 3.85786554e-10],\n",
       "          [9.99911514e-02, 3.85786764e-10],\n",
       "          [9.99911514e-02, 3.85786764e-10],\n",
       "          [9.99911514e-02, 3.85786959e-10],\n",
       "          [9.99911514e-02, 3.85786959e-10],\n",
       "          [9.99911514e-02, 3.85787122e-10],\n",
       "          [9.99911514e-02, 3.85787122e-10],\n",
       "          [9.99911514e-02, 3.85787122e-10],\n",
       "          [9.99911514e-02, 3.85787247e-10],\n",
       "          [9.99911514e-02, 3.85787247e-10],\n",
       "          [9.99911514e-02, 3.85787247e-10],\n",
       "          [9.99911514e-02, 3.85787247e-10],\n",
       "          [9.99911514e-02, 3.85787322e-10],\n",
       "          [9.99911514e-02, 3.85787322e-10],\n",
       "          [9.99911514e-02, 3.85787375e-10],\n",
       "          [9.99911514e-02, 3.85787375e-10],\n",
       "          [9.99911514e-02, 3.85787401e-10],\n",
       "          [9.99911514e-02, 3.85787401e-10],\n",
       "          [9.99911514e-02, 3.85785465e-10],\n",
       "          [9.99911514e-02, 3.85785465e-10],\n",
       "          [9.99911514e-02, 3.85785465e-10],\n",
       "          [9.99911514e-02, 3.85785618e-10],\n",
       "          [9.99911514e-02, 3.85785618e-10],\n",
       "          [9.99911514e-02, 3.85785618e-10],\n",
       "          [9.99911514e-02, 3.85785618e-10],\n",
       "          [9.99911514e-02, 3.85785717e-10],\n",
       "          [9.99911514e-02, 3.85785717e-10],\n",
       "          [9.99911514e-02, 3.85785717e-10],\n",
       "          [9.99911514e-02, 3.85785717e-10],\n",
       "          [9.99911514e-02, 3.85785768e-10],\n",
       "          [9.99911514e-02, 3.85785768e-10],\n",
       "          [9.99911514e-02, 3.85785768e-10],\n",
       "          [9.99911514e-02, 3.85785768e-10],\n",
       "          [9.99911514e-02, 3.85785440e-10],\n",
       "          [9.99911514e-02, 3.85785440e-10],\n",
       "          [9.99911514e-02, 3.85785440e-10],\n",
       "          [9.99911514e-02, 3.85785440e-10],\n",
       "          [9.99911514e-02, 3.85785395e-10],\n",
       "          [9.99911514e-02, 3.85785395e-10],\n",
       "          [9.99911514e-02, 3.85785395e-10],\n",
       "          [9.99911514e-02, 3.85785446e-10],\n",
       "          [9.99911514e-02, 3.85785446e-10],\n",
       "          [9.99911514e-02, 3.85785446e-10],\n",
       "          [9.99911514e-02, 3.85785446e-10],\n",
       "          [9.99911514e-02, 3.85786562e-10],\n",
       "          [9.99911514e-02, 3.85786562e-10],\n",
       "          [9.99911514e-02, 3.85786562e-10],\n",
       "          [9.99911514e-02, 3.85786562e-10],\n",
       "          [9.99911514e-02, 3.85786798e-10],\n",
       "          [9.99911514e-02, 3.85786798e-10],\n",
       "          [9.99911514e-02, 3.85786798e-10],\n",
       "          [9.99911514e-02, 3.85786798e-10],\n",
       "          [9.99911514e-02, 3.85786988e-10],\n",
       "          [9.99911514e-02, 3.85786988e-10],\n",
       "          [9.99911514e-02, 3.85786988e-10],\n",
       "          [9.99911514e-02, 3.85787140e-10],\n",
       "          [9.99911514e-02, 3.85787140e-10],\n",
       "          [9.99911514e-02, 3.85787266e-10],\n",
       "          [9.99911514e-02, 3.85787266e-10],\n",
       "          [9.99911514e-02, 3.85787369e-10],\n",
       "          [9.99911514e-02, 3.85787369e-10],\n",
       "          [9.99911514e-02, 3.85787444e-10],\n",
       "          [9.99911514e-02, 3.85787444e-10],\n",
       "          [9.99911514e-02, 3.85787495e-10],\n",
       "          [9.99911514e-02, 3.85787495e-10],\n",
       "          [9.99911514e-02, 3.85787495e-10],\n",
       "          [9.99911514e-02, 3.85787495e-10]]),\n",
       "   'nodes': array([1.00118296, 1.00118296, 1.00118296, 1.00118296, 1.00118296,\n",
       "          1.00118296, 1.00118296, 1.00118296, 1.00118296, 1.00118296,\n",
       "          1.00118296, 1.00118296, 1.00118296, 1.00118296, 1.00118296,\n",
       "          1.00118296, 1.00118296, 1.00118296, 1.00118296, 1.00118296,\n",
       "          1.00118296, 1.00118296, 1.00118296, 1.00118296, 1.00118296,\n",
       "          1.00118296, 1.00118296, 1.00118296, 1.00118296, 1.00118296,\n",
       "          1.00118296, 1.00118296, 1.00118296])}],\n",
       " 'additional_feature_keys': [],\n",
       " 'snapshot_count': 23}"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_snapshots.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric_temporal.nn.hetero import HeteroGCLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.nn import Parameter\n",
    "from torch_geometric.nn import HeteroConv, SAGEConv\n",
    "from torch_geometric.nn.inits import glorot\n",
    "import torch.nn as nn\n",
    "\n",
    "class HeteroGCLSTM(torch.nn.Module):\n",
    "    r\"\"\"An implementation similar to the Integrated Graph Convolutional Long Short Term\n",
    "        Memory Cell for heterogeneous Graphs.\n",
    "\n",
    "        Args:\n",
    "            in_channels_dict (dict of keys=str and values=int): Dimension of each node's input features.\n",
    "            out_channels (int): Number of output features.\n",
    "            metadata (tuple): Metadata on node types and edge types in the graphs. Can be generated via PyG method\n",
    "                :obj:`snapshot.metadata()` where snapshot is a single HeteroData object.\n",
    "            bias (bool, optional): If set to :obj:`False`, the layer will not learn\n",
    "                an additive bias. (default: :obj:`True`)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "            self,\n",
    "            in_channels_dict: dict,\n",
    "            out_channels: int,\n",
    "            metadata: tuple,\n",
    "            bias: bool = True\n",
    "    ):\n",
    "        super(HeteroGCLSTM, self).__init__()\n",
    "\n",
    "        self.in_channels_dict = in_channels_dict\n",
    "        self.out_channels = out_channels\n",
    "        self.metadata = metadata\n",
    "        self.bias = bias\n",
    "        self._create_parameters_and_layers()\n",
    "        self._set_parameters()\n",
    "\n",
    "    def _create_input_gate_parameters_and_layers(self):\n",
    "        self.conv_i = HeteroConv({edge_type: SAGEConv(in_channels=(-1, -1),\n",
    "                                                      out_channels=self.out_channels,\n",
    "                                                      bias=self.bias) for edge_type in self.metadata[1]})\n",
    "\n",
    "        self.W_i = nn.ParameterDict({node_type: Parameter(torch.Tensor(in_channels, self.out_channels))\n",
    "                    for node_type, in_channels in self.in_channels_dict.items()})\n",
    "        self.b_i = nn.ParameterDict({node_type: Parameter(torch.Tensor(1, self.out_channels))\n",
    "                    for node_type in self.in_channels_dict})\n",
    "\n",
    "    def _create_forget_gate_parameters_and_layers(self):\n",
    "        self.conv_f = HeteroConv({edge_type: SAGEConv(in_channels=(-1, -1),\n",
    "                                                      out_channels=self.out_channels,\n",
    "                                                      bias=self.bias) for edge_type in self.metadata[1]})\n",
    "\n",
    "        self.W_f = nn.ParameterDict({node_type: Parameter(torch.Tensor(in_channels, self.out_channels))\n",
    "                    for node_type, in_channels in self.in_channels_dict.items()})\n",
    "        self.b_f = nn.ParameterDict({node_type: Parameter(torch.Tensor(1, self.out_channels))\n",
    "                    for node_type in self.in_channels_dict})\n",
    "\n",
    "    def _create_cell_state_parameters_and_layers(self):\n",
    "        self.conv_c = HeteroConv({edge_type: SAGEConv(in_channels=(-1, -1),\n",
    "                                                      out_channels=self.out_channels,\n",
    "                                                      bias=self.bias) for edge_type in self.metadata[1]})\n",
    "\n",
    "        self.W_c = nn.ParameterDict({node_type: Parameter(torch.Tensor(in_channels, self.out_channels))\n",
    "                    for node_type, in_channels in self.in_channels_dict.items()})\n",
    "        self.b_c = nn.ParameterDict({node_type: Parameter(torch.Tensor(1, self.out_channels))\n",
    "                    for node_type in self.in_channels_dict})\n",
    "\n",
    "    def _create_output_gate_parameters_and_layers(self):\n",
    "        self.conv_o = HeteroConv({edge_type: SAGEConv(in_channels=(-1, -1),\n",
    "                                                      out_channels=self.out_channels,\n",
    "                                                      bias=self.bias) for edge_type in self.metadata[1]})\n",
    "\n",
    "        self.W_o = nn.ParameterDict({node_type: Parameter(torch.Tensor(in_channels, self.out_channels))\n",
    "                    for node_type, in_channels in self.in_channels_dict.items()})\n",
    "        self.b_o = nn.ParameterDict({node_type: Parameter(torch.Tensor(1, self.out_channels))\n",
    "                    for node_type in self.in_channels_dict})\n",
    "\n",
    "    def _create_parameters_and_layers(self):\n",
    "        self._create_input_gate_parameters_and_layers()\n",
    "        self._create_forget_gate_parameters_and_layers()\n",
    "        self._create_cell_state_parameters_and_layers()\n",
    "        self._create_output_gate_parameters_and_layers()\n",
    "\n",
    "    def _set_parameters(self):\n",
    "        for key in self.W_i:\n",
    "            glorot(self.W_i[key])\n",
    "        for key in self.W_f:\n",
    "            glorot(self.W_f[key])\n",
    "        for key in self.W_c:\n",
    "            glorot(self.W_c[key])\n",
    "        for key in self.W_o:\n",
    "            glorot(self.W_o[key])\n",
    "        for key in self.b_i:\n",
    "            glorot(self.b_i[key])\n",
    "        for key in self.b_f:\n",
    "            glorot(self.b_f[key])\n",
    "        for key in self.b_c:\n",
    "            glorot(self.b_c[key])\n",
    "        for key in self.b_o:\n",
    "            glorot(self.b_o[key])\n",
    "\n",
    "    def _set_hidden_state(self, x_dict, h_dict):\n",
    "        if h_dict is None:\n",
    "            h_dict = {node_type: torch.zeros(X.shape[0], self.out_channels) for node_type, X in x_dict.items()}\n",
    "        return h_dict\n",
    "\n",
    "    def _set_cell_state(self, x_dict, c_dict):\n",
    "        if c_dict is None:\n",
    "            c_dict = {node_type: torch.zeros(X.shape[0], self.out_channels) for node_type, X in x_dict.items()}\n",
    "        return c_dict\n",
    "\n",
    "    def _calculate_input_gate(self, x_dict, edge_index_dict, h_dict, c_dict):\n",
    "        i_dict = {node_type: torch.matmul(X, self.W_i[node_type]) for node_type, X in x_dict.items()}\n",
    "        conv_i = self.conv_i(h_dict, edge_index_dict)\n",
    "        i_dict = {node_type: I + conv_i[node_type] for node_type, I in i_dict.items()}\n",
    "        i_dict = {node_type: I + self.b_i[node_type] for node_type, I in i_dict.items()}\n",
    "        i_dict = {node_type: torch.sigmoid(I) for node_type, I in i_dict.items()}\n",
    "        return i_dict\n",
    "\n",
    "    def _calculate_forget_gate(self, x_dict, edge_index_dict, h_dict, c_dict):\n",
    "        f_dict = {node_type: torch.matmul(X, self.W_f[node_type]) for node_type, X in x_dict.items()}\n",
    "        conv_f = self.conv_f(h_dict, edge_index_dict)\n",
    "        f_dict = {node_type: F + conv_f[node_type] for node_type, F in f_dict.items()}\n",
    "        f_dict = {node_type: F + self.b_f[node_type] for node_type, F in f_dict.items()}\n",
    "        f_dict = {node_type: torch.sigmoid(F) for node_type, F in f_dict.items()}\n",
    "        return f_dict\n",
    "\n",
    "    def _calculate_cell_state(self, x_dict, edge_index_dict, h_dict, c_dict, i_dict, f_dict):\n",
    "        t_dict = {node_type: torch.matmul(X, self.W_c[node_type]) for node_type, X in x_dict.items()}\n",
    "        conv_c = self.conv_c(h_dict, edge_index_dict)\n",
    "        t_dict = {node_type: T + conv_c[node_type] for node_type, T in t_dict.items()}\n",
    "        t_dict = {node_type: T + self.b_c[node_type] for node_type, T in t_dict.items()}\n",
    "        t_dict = {node_type: torch.tanh(T) for node_type, T in t_dict.items()}\n",
    "        c_dict = {node_type: f_dict[node_type] * C + i_dict[node_type] * t_dict[node_type] for node_type, C in c_dict.items()}\n",
    "        return c_dict\n",
    "\n",
    "    def _calculate_output_gate(self, x_dict, edge_index_dict, h_dict, c_dict):\n",
    "        o_dict = {node_type: torch.matmul(X, self.W_o[node_type]) for node_type, X in x_dict.items()}\n",
    "        conv_o = self.conv_o(h_dict, edge_index_dict)\n",
    "        o_dict = {node_type: O + conv_o[node_type] for node_type, O in o_dict.items()}\n",
    "        o_dict = {node_type: O + self.b_o[node_type] for node_type, O in o_dict.items()}\n",
    "        o_dict = {node_type: torch.sigmoid(O) for node_type, O in o_dict.items()}\n",
    "        return o_dict\n",
    "\n",
    "    def _calculate_hidden_state(self, o_dict, c_dict):\n",
    "        h_dict = {node_type: o_dict[node_type] * torch.tanh(C) for node_type, C in c_dict.items()}\n",
    "        return h_dict\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        x_dict,\n",
    "        edge_index_dict,\n",
    "        h_dict=None,\n",
    "        c_dict=None,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Making a forward pass. If the hidden state and cell state\n",
    "        matrix dicts are not present when the forward pass is called these are\n",
    "        initialized with zeros.\n",
    "\n",
    "        Arg types:\n",
    "            * **x_dict** *(Dictionary where keys=Strings and values=PyTorch Float Tensors)* - Node features dicts. Can\n",
    "                be obtained via PyG method :obj:`snapshot.x_dict` where snapshot is a single HeteroData object.\n",
    "            * **edge_index_dict** *(Dictionary where keys=Tuples and values=PyTorch Long Tensors)* - Graph edge type\n",
    "                and index dicts. Can be obtained via PyG method :obj:`snapshot.edge_index_dict`.\n",
    "            * **h_dict** *(Dictionary where keys=Strings and values=PyTorch Float Tensor, optional)* - Node type and\n",
    "                hidden state matrix dict for all nodes.\n",
    "            * **c_dict** *(Dictionary where keys=Strings and values=PyTorch Float Tensor, optional)* - Node type and\n",
    "                cell state matrix dict for all nodes.\n",
    "\n",
    "        Return types:\n",
    "            * **h_dict** *(Dictionary where keys=Strings and values=PyTorch Float Tensor)* - Node type and\n",
    "                hidden state matrix dict for all nodes.\n",
    "            * **c_dict** *(Dictionary where keys=Strings and values=PyTorch Float Tensor)* - Node type and\n",
    "                cell state matrix dict for all nodes.\n",
    "        \"\"\"\n",
    "\n",
    "        h_dict = self._set_hidden_state(x_dict, h_dict)\n",
    "        c_dict = self._set_cell_state(x_dict, c_dict)\n",
    "        i_dict = self._calculate_input_gate(x_dict, edge_index_dict, h_dict, c_dict)\n",
    "        f_dict = self._calculate_forget_gate(x_dict, edge_index_dict, h_dict, c_dict)\n",
    "        c_dict = self._calculate_cell_state(x_dict, edge_index_dict, h_dict, c_dict, i_dict, f_dict)\n",
    "        o_dict = self._calculate_output_gate(x_dict, edge_index_dict, h_dict, c_dict)\n",
    "        h_dict = self._calculate_hidden_state(o_dict, c_dict)\n",
    "        return h_dict, c_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Dell\\miniconda3\\envs\\pinss\\lib\\site-packages\\torch_geometric\\nn\\conv\\hetero_conv.py:62: UserWarning: There exist node types ({'cons'}) whose representations do not get updated during message passing as they do not occur as destination type in any edge type. This may lead to unexpected behavior.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "mat1 and mat2 shapes cannot be multiplied (1x33 and 1x3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[149], line 48\u001b[0m\n\u001b[0;32m     45\u001b[0m edge_index_dict \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39medge_index_dict\n\u001b[0;32m     47\u001b[0m \u001b[39m# Forward pass\u001b[39;00m\n\u001b[1;32m---> 48\u001b[0m h_dict, c_dict \u001b[39m=\u001b[39m model(snapshot\u001b[39m.\u001b[39;49mx_dict, snapshot\u001b[39m.\u001b[39;49medge_index_dict,h_dict,c_dict)\n\u001b[0;32m     50\u001b[0m \u001b[39m# Compute loss\u001b[39;00m\n\u001b[0;32m     51\u001b[0m loss \u001b[39m=\u001b[39m criterion(h_dict[\u001b[39m'\u001b[39m\u001b[39mcons\u001b[39m\u001b[39m'\u001b[39m], snapshot\u001b[39m.\u001b[39my[\u001b[39m'\u001b[39m\u001b[39mcons\u001b[39m\u001b[39m'\u001b[39m])\n",
      "File \u001b[1;32mc:\\Users\\Dell\\miniconda3\\envs\\pinss\\lib\\site-packages\\torch\\nn\\modules\\module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "Cell \u001b[1;32mIn[132], line 178\u001b[0m, in \u001b[0;36mHeteroGCLSTM.forward\u001b[1;34m(self, x_dict, edge_index_dict, h_dict, c_dict)\u001b[0m\n\u001b[0;32m    176\u001b[0m h_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_set_hidden_state(x_dict, h_dict)\n\u001b[0;32m    177\u001b[0m c_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_set_cell_state(x_dict, c_dict)\n\u001b[1;32m--> 178\u001b[0m i_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_calculate_input_gate(x_dict, edge_index_dict, h_dict, c_dict)\n\u001b[0;32m    179\u001b[0m f_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_calculate_forget_gate(x_dict, edge_index_dict, h_dict, c_dict)\n\u001b[0;32m    180\u001b[0m c_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_calculate_cell_state(x_dict, edge_index_dict, h_dict, c_dict, i_dict, f_dict)\n",
      "Cell \u001b[1;32mIn[132], line 111\u001b[0m, in \u001b[0;36mHeteroGCLSTM._calculate_input_gate\u001b[1;34m(self, x_dict, edge_index_dict, h_dict, c_dict)\u001b[0m\n\u001b[0;32m    110\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_calculate_input_gate\u001b[39m(\u001b[39mself\u001b[39m, x_dict, edge_index_dict, h_dict, c_dict):\n\u001b[1;32m--> 111\u001b[0m     i_dict \u001b[39m=\u001b[39m {node_type: torch\u001b[39m.\u001b[39mmatmul(X, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mW_i[node_type]) \u001b[39mfor\u001b[39;00m node_type, X \u001b[39min\u001b[39;00m x_dict\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m    112\u001b[0m     conv_i \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv_i(h_dict, edge_index_dict)\n\u001b[0;32m    113\u001b[0m     i_dict \u001b[39m=\u001b[39m {node_type: I \u001b[39m+\u001b[39m conv_i[node_type] \u001b[39mfor\u001b[39;00m node_type, I \u001b[39min\u001b[39;00m i_dict\u001b[39m.\u001b[39mitems()}\n",
      "Cell \u001b[1;32mIn[132], line 111\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    110\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_calculate_input_gate\u001b[39m(\u001b[39mself\u001b[39m, x_dict, edge_index_dict, h_dict, c_dict):\n\u001b[1;32m--> 111\u001b[0m     i_dict \u001b[39m=\u001b[39m {node_type: torch\u001b[39m.\u001b[39;49mmatmul(X, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mW_i[node_type]) \u001b[39mfor\u001b[39;00m node_type, X \u001b[39min\u001b[39;00m x_dict\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m    112\u001b[0m     conv_i \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv_i(h_dict, edge_index_dict)\n\u001b[0;32m    113\u001b[0m     i_dict \u001b[39m=\u001b[39m {node_type: I \u001b[39m+\u001b[39m conv_i[node_type] \u001b[39mfor\u001b[39;00m node_type, I \u001b[39min\u001b[39;00m i_dict\u001b[39m.\u001b[39mitems()}\n",
      "\u001b[1;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (1x33 and 1x3)"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch_geometric.data import DataLoader\n",
    "from torch.optim import Adam\n",
    "from torch.nn import MSELoss\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "# Set device\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "\n",
    "in_channels_dict = {\n",
    "    \"cons\": 2,\n",
    "    \"nodes\": 1\n",
    "}\n",
    "\n",
    "h_dict = {\n",
    "    \"cons\": 0,\n",
    "    \"nodes\": 0\n",
    "}\n",
    "\n",
    "c_dict = {\n",
    "    \"cons\": 2,\n",
    "    \"nodes\": 1\n",
    "}\n",
    "out_channels = 3\n",
    "# Create an instance of the HeteroGCLSTM model\n",
    "model = HeteroGCLSTM(in_channels_dict, out_channels, metadata).to(device)\n",
    "\n",
    "# Set loss function and optimizer\n",
    "criterion = MSELoss()\n",
    "optimizer = Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# Training loop\n",
    "model.train()\n",
    "num_epochs = 10\n",
    "for epoch in range(num_epochs):\n",
    "    total_loss = 0\n",
    "\n",
    "    for time, snapshot in enumerate(train_dataset):\n",
    "        snapshot = snapshot.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "\n",
    "        # Forward pass\n",
    "        h_dict, c_dict = model(snapshot.x_dict, snapshot.edge_index_dict)\n",
    "\n",
    "        # Compute loss\n",
    "        loss = criterion(h_dict['cons'], snapshot.y['cons'])\n",
    "\n",
    "        # Backward pass and optimization\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    # Compute average loss for the epoch\n",
    "    avg_loss = total_loss / len(train_dataset)\n",
    "\n",
    "    print(f'Epoch: {epoch + 1}, Loss: {avg_loss}')\n",
    "\n",
    "# Evaluation loop\n",
    "model.eval()\n",
    "total_test_loss = 0\n",
    "\n",
    "for time, snapshot in enumerate(test_dataset):\n",
    "    data = snapshot.to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        h_dict, c_dict = model(snapshot.x_dict, snapshot.edge_index_dict)\n",
    "        loss = criterion(h_dict['cons'], data.y['cons'])\n",
    "        total_test_loss += loss.item()\n",
    "\n",
    "# Compute average test loss\n",
    "avg_test_loss = total_test_loss / len(test_dataset)\n",
    "print(f'Test Loss: {avg_test_loss}')"
   ]
  }
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